SNS Retrieval Based on User Profile Estimation Using Transfer Learning from Web Search

Daisuke Kataoka, Keishi Tajima
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引用次数: 1

Abstract

In this paper, we propose a method of retrieving posts on social networking services (SNSs) by specifying a pair of queries: a topic query and an entity query. A topic query specifies the topic of the posts to retrieve (e.g., "iPhone") and an entity query specifies the type of users who posted them (e.g., "students"). In the existing search systems for SNS posts, we can specify topics of posts by keywords, but we cannot specify types of users. Even if we include keywords specifying types of users in a query, such keywords are not usually included in tweets or user profile data. In our method, we estimate types of users by learning vocabulary whose appearance is correlated with specific types of users. We learn it from the datasets obtained through Web search. We retrieve Web documents through the search with a keyword specifying the type of users (e.g., "student"), and we also retrieve Web documents by using a keyword specifying its opposite (e.g., "adult"). We regard the documents retrieved by these queries as positive and negative examples of documents describing the target type, and we train a model for recognizing users of the given type. We recognize users of the target type by inputting their posts and their profile data into the model. We use Web documents instead of SNS posts for training the model because the Web has more documents describing types of people.
基于Web搜索迁移学习的用户轮廓估计的SNS检索
在本文中,我们提出了一种通过指定一对查询:主题查询和实体查询来检索社交网络服务(sns)上的帖子的方法。主题查询指定要检索的帖子的主题(例如,“iPhone”),实体查询指定发布帖子的用户类型(例如,“学生”)。在现有的SNS帖子搜索系统中,我们可以通过关键词来指定帖子的主题,但是不能指定用户的类型。即使我们在查询中包含指定用户类型的关键字,这些关键字通常也不会包含在tweet或用户个人资料数据中。在我们的方法中,我们通过学习与特定用户类型相关的词汇来估计用户类型。我们从通过网络搜索获得的数据集中学习它。我们通过使用指定用户类型的关键字(例如,“学生”)进行搜索来检索Web文档,我们也通过使用指定相反类型的关键字(例如,“成人”)来检索Web文档。我们将这些查询检索到的文档视为描述目标类型的文档的正反例,并训练一个模型来识别给定类型的用户。我们通过在模型中输入他们的帖子和个人资料数据来识别目标类型的用户。我们使用Web文档而不是SNS帖子来训练模型,因为Web上有更多描述人类型的文档。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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